Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration

Abstract

BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.

Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG–fMRI Investigation

“…It is therefore of vital importance to find a way to fuse complementary different brain imaging techniques [ 12 , 13 ], such as the emerging field of deep learning-based fusion [ 14 ]. Currently, there are three main EEG–fMRI fusion methods: EEG source localization based on fMRI constraints [ 15 , 16 ]; fMRI prediction based on EEG information [ 17 ]; and symmetric fusion methods that simultaneously interpret data from both modalities [ 18 ]. Among these, the symmetric fusion methods are mainly used to separate the sources of EEG and fMRI data using joint decomposition methods such as ICA and CCA [ 19 ].…”

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

“…Therefore, EEG has been widely used in the study of neural mechanisms and clinical diagnosis of mental diseases [e.g., depression ( Feng et al, 2012 ; Jesulola et al, 2015 ; Steiger and Pawlowski, 2019 ), anxiety disorder ( Tyrer and Baldwin, 2006 ; Olatunji et al, 2010 ; Hilbert et al, 2014 ), sleep disorder ( Papadimitriou et al, 1988 ; Huang et al, 2019 ), and epilepsy ( Mporas et al, 2015 )]. Common EEG analysis methods include power spectral density (PSD) analysis ( Fenton et al, 1980 ; Pradhan and Dutt, 1994 ) [e.g., absolute power (AP), relative power, and power ratio], non-linear dynamics analysis ( Nikias and Petropulu, 1993 ; Alotaibi et al, 2022 ) [e.g., approximate entropy, fuzzy entropy (FE), and sample entropy], and brain functional connectivity (FC) analysis ( Stam et al, 2007 ; Piho and Tjahjadi, 2020 ; Li et al, 2022 ) [e.g., Partial directed coherence (PDC), mutual information (MI), and phase-lag-index (PLI)]. In this study, the PSD, FE, and PLI were used as the main methods to analyze EEG data and to reveal the electrophysiological differences of GAD in different age groups.…”